Systems are available to monitor and record patient physiological signals. For example, ECG monitoring systems such as Holter monitors are available to be worn externally by a patient to record electrical signals produced by the heart. Similar systems exist for monitoring and recording EEG signals from the brain of a patient. Other ElectroGraM (EGM) monitors may be implanted within the body of a patient to record similar types of physiological signals. Such monitoring systems are described in U.S. Pat. No. 5,312,446 and in U.S. Pat. No. 4,947,858, both of which are assigned to the assignee of the current invention, and are incorporated herein by reference.
Monitoring systems often have storage limitations. Cost, size, power consumption, and the sheer volume of data over time have limited physiological signal monitors to recording relatively brief segments of data. As an alternative, multiple shorter segments of data may be recorded when an abnormal signal is automatically detected by the monitor, when manually initiated, or at periodic intervals. Storage constraints are even more pronounced in an implantable monitoring system, wherein conserving both power and space are prime considerations. An Implantable Medical Device (IMD) may be adapted to include a physiological signal monitoring system and may also be adapted to provide a therapy in response to the detection of certain abnormal signals.
An IMD may store physiological signal segments and event data in internal RAM. This data may also be transferred to an external programmer via a telemetry link, for example, using radio frequency (RF) communications. The stored data may be periodically transmitted from device memory to an external programmer, or second implanted device, via a telemetry session. The amount of time required to transmit a given amount of physiological signal data from an IMD's memory to an external system may impose another constraint on the data storage capabilities of the IMD.
Other types of physiological signals may also be stored by recording systems. Such signals include blood pressure signals associated with the heart chamber or adjoining blood vessels during the cardiac cycle. Blood temperature, blood pH, and a variety of blood gas-level indications may also be recorded. Recording systems for monitoring these types of signals are disclosed in commonly assigned U.S. Pat. Nos. 5,368,040, 5,535,752 and 5,564,434, and in U.S. Pat. No. 4,791,931, all incorporated by reference herein by reference. The MEDTRONIC® Chronicle® implantable hemodynamic recorder employs the leads and circuitry disclosed in the above-incorporated, commonly assigned, '752 and '434 patents to record the EGM and absolute blood pressure values for certain intervals. The recorded data is periodically transferred to an external programmer via an uplink telemetry transmission.
Physiological signals of the type recorded by IMDs and external monitors are typically sensed by electrodes. The signals are then filtered, amplified, digitized, and stored in memory at a selected sampling frequency. The sampling frequency is selected based on the frequency content of the particular physiological signal. Generally, a high enough sampling frequency is selected so that an accurate signal may be reconstructed and displayed later. At a sampling frequency of about 256 Hz, for example, enough information may be retained to accurately reconstruct visual displays of most physiological signal data. However, at this sampling rate, approximately twenty-two megabytes of storage space is required to record signal data over a twenty-four-hour period, assuming each sample is stored as an 8-bit byte. At this rate, the storage requirements for even short signal data segments become prohibitive in IMDs, given the inherent space and power constraints. Moreover, it may take up to five hours to transfer a twenty-four hour data segment, depending on the telemetry transmission techniques employed.
Because of these constraints, attempts have been made to lower sampling rates, and/or to compress the sampled data. Reducing the sampling rate from 256 Hz to 128 Hz conserves available memory by effectively removing half of the input data. In signals consisting of frequency components not greater than about 60 Hz, this sampling rate may be adequate. However, some waveforms contain energy above 60 Hz. In these cases, the reduction in sampling rate may result in a loss of data. This may result in the loss of slope and shape information of the original waveform, potentially making it difficult or impossible for the medical care providers to use the information to make a correct diagnosis.
Some data compression techniques provide an alternative to merely reducing the sampling frequency. These techniques can be characterized as either “lossy” or “loss-less.” When data is compressed using a “loss-less” method, it is possible to reconstruct the original waveform without losing information. Such non-distorting compression modes are exemplified by the Huffman coding method and the Lempel-Ziv method, as described respectively in the following articles: Huffman, D. A., “A method for the construction of minimum-redundancy codes,” Proc. IEEE, 40:1098-1101, 1952; and Ziv, J. and Lempel, A., “A universal algorithm for sequential data compression,” IEEE Trans. Inform. Theory, IT-23, pp. 337-343, 1977.
Loss-less compression modes are computationally expensive, resulting in a more complex circuit design, increased power consumption, and a longer data processing time. These techniques may also require a considerable amount of memory to perform. Moreover, the compression rate that is achieved depends on the content of the physiological signals. The content will vary depending on the physiological condition being monitored. As a result, the amount of memory needed to store the compressed data cannot be determined in advance.
As an alternative to the use of a loss-less compression process, a lossy technique may be employed. Lossy techniques may require less processing power to implement. The resulting system may therefore be both smaller and more energy efficient. However, when data is compressed using a lossy method, some information is lost when the compressed data is later used to re-construct the original waveform. According to one approach, all “baseline” sample values of a signal are discarded. This can greatly reduce memory requirements but may not be acceptable, however, because many types of signals include virtually no baseline segments. Moreover, these types of signals may be the signals of greatest interest during the diagnosis of an abnormal condition.
Other “lossy” compression methods have been used, for example, in ECG storage in external cardiac monitoring devices. Examples of such techniques include the Amplitude-Zone-Epoch-Time-Coding (AZTEC) pre-processing software program. AZTEC is described in “AZTEC, a pre-processing program for real-time ECG rhythm analysis” by Cox, J. R. et al. (IEEE Trans. Biomed. Eng., BME-15, pp. 128-129., 1968).
Another lossy technique is the SAPA, or “fan” method described in the above-incorporated '858 patent, and further discussed in “Scan-along polygonal approximation for data compression of electrocardiograms,” by Ishijima, M. et al. (IEEE Trans. Biomed. Eng., BME-26, pp. 723-729, 1983). However, this method uses a straight-line approximation of the waveform to store the ECG data and does not generally permit substantial data compression.
Yet another technique is referred to as the Coordinate-Reduction-Time-Encoding-System (CORTES). This mechanism may be used to achieve up to 10:1 data compression, as is described in “Data Compression--Techniques and Applications,” Pg. 256-259, T. J. Lynch. However, this technique is not considered adaptable for use in IMDs because of the processing power required to compress and store the data points in real time.
Other lossy data compression techniques are based upon domain transforms and include the Discrete Cosine Transform (DCT), the Walsh Transform, the Harr Wavelet Transform and the Daubechies Wavelet Transform. These techniques are relatively computationally intensive and are batch oriented. This means that these methods operate on a block of sequential data points at one time, and therefore are not easily implemented in integrated circuit hardware.
Another lossy compression mechanism is called the Mueller “turning point” algorithm, as described by W. C. Mueller in “Arrhythmia detection program for an ambulatory ECG monitor” (Biomed. Sci. Instrum. 14: 81-85, 1978). This algorithm achieves a fixed two-to-one compression ratio. The algorithm utilizes the last saved data point (X0) to determine which of the next two data points X1 or X2 to retain, where X2 is the data point more recent in time. According to this algorithm, X1 will be retained if that data point is a “turning” point, meaning that the slope of the line between points X0 and X1 is positive, whereas the slope of the line between X1 and X2 is negative, or vice versa. In other words, X1 is retained if it is a “peak” or a “valley” in the waveform. Otherwise, X2 is retained.
The turning point algorithm is relatively simple to implement in either hardware, software, or a combination thereof Moreover, even though the algorithm is lossy, the compressed data can, in many instances, be used to reconstruct a signal that retains the important peaks and valleys of a physiological signal. If greater than two-to-one compression is desired, the output of one turning point process may be cascaded with another compression iteration to achieve four-to-one compression.
Although the turning point mechanism provides many important advantages over other data compression schemes, in some instances, the turning point algorithm can discard important information. This occurs when both X1 and X2 are turning points, and X2 is a more important turning point than X1. For example, X1 and X2 may represent the Q and R waves of the QRS complex of an ECG signal, respectively. The turning point algorithm will retain the smaller Q wave and discard the larger R wave. When the EGM or ECG trace is reconstructed from the compressed data, both the amplitude and the general shape of the resulting waveform may be distorted. The effects are compounded when the turning point algorithm is cascaded to provide four-to-one data compression.
An improved turning point data compression method is disclosed in U.S. Pat. No. 6,599,242 to Splett et al., assigned to the assignee of the present invention. The improvement includes selecting a predetermined number of “best” turning points in the sample window, with a variety of criteria for selecting the best turning points for retention.
What is needed is an improved method and apparatus for data compression adaptable for use in an IMD or external monitor having limited memory capacity. This method and apparatus should provide a sufficiently high compression factor, should not require large amounts of processing power to complete, and should provide reconstructed waveforms that are clinically acceptable.
Further, the need exists for a data compression method for use in an IMD having limited memory, for monitoring and possibly also treating certain abnormal conditions, such as epileptic seizures, etc. The data compression method should provide a sufficiently high compression factor, and should maintain accurate frequency information, possibly at the expense of accurate amplitude information. The method should not require large amounts of computational processing power, and should produce reconstructed waveforms that are clinically acceptable. The method and apparatus for data compression may be lossy, but should not lose any temporal precision, and should attempt to provide predictable fixed compression ratios.